Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it. KEY WORDS: cognition; instructional design; learning; problem solving.

Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom in-struction and the broader educational research community. A frame-work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional de-sign principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and em-phasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each concep-tual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More-over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain examples. Worked examples are associated with early stages of skill develop-ment, but the design principles are relevant to constructivist research and teaching. The Historical Context In recent years, learning from "worked examples " has received a consider-able amount of attention from researchers (e.g., Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Ward & Sweller, 1990), particularly in such fields as mathematics, physics, and computer programming. Although there is no precise definition, worked examples share certain family resemblance (Wittgenstein, 1953). As instructional devices, they typically include a problem statement and a proce-dure for solving the problem; together, these are meant to show how other similar problems might be solved. In a sense, they provide an expert's problem-

... importance for initial skill acquisition in well-structured domains. In addition, research has provided knowledge in regards to structuring worked-out examples and how to effectively combine self-explanation activity and instructional explanations. The goal of the present project was to develop a computer-based learning environment in which teachers can learn how to use worked-out examples. Examples of favorably and unfavorably designed worked-out examples were the primary source of information for the teachers. The examples (of worked-out examples) were not in themselves worked-out examples if one views them from a design perspective as the (design) solution steps were not given. We have labeled this type of examples "solved example problems." We investigated to what extent learning from such solved example problems could be fostered by self-explanation prompts and by providing instructional explanations. The results of our 2x2 design (80 student teachers) showed that prompting selfexplanations in particular had favorable effects. Hence, self-explanations fostered learning not only from worked-out examples but also from solved example problems. Supplementary instructional explanations only partially enhanced learning and at times they were even detrimental.

Although problem solving supported by Cognitive Tutors has been shown to be successful in fostering initial acquisition of cognitive skills, this approach does not seem to be optimal with respect to focusing the learner on the domain principles to be learned. In order to foster a deep understanding of domain principles, we developed a Cognitive Tutor that contained, on the basis of the theoretical rational of examplebased learning, faded worked-out examples. We conducted two experiments in which we compared the example-enriched Cognitive Tutor with a standard Cognitive Tutor. In Experiment 1, we found no significant differences in the effectiveness of the two tutor versions. However, the example-enriched Cogntive Tutor was more efficient (i.e., students needed less learning time). A problem that was observed is that students had great problems in appropriately using the example-enriched tutor. In Experiment 2, we, therefore, provided students with additional instructions on how to use the tutor. Results showed that students in fact acquired a deeper conceptual understanding when they worked with the example-enriched tutor and they needed less learning time than in the standard Tutor. The results are suggestive of ways in which instructional models of problemsolving and example-based learning can be fruitfully combined.

Despite the huge production of all sorts of multimedia instructions, educational research has yielded surprisingly few general design principles for instructions in which verbal and visual information are combined. Instructional designers seem to base their design choices more on intuitive ideas than on sound research results. There are however some interesting theories that give guidelines for instructional designers. Mayer (1997) describes in his Generative Theory of Multimedia Learning how the learner builds mental representations of multimedia material. One important step in this process is the integration of both visual and verbal information. Multimedia instruction in its most elementary form consists of a picture with an explanatory text. Because picture and text cannot be perceived simultaneously, the learner is forced to switch back and forth between the two and integrate them mentally. According to Cognitive Load Theory (Sweller, 1988; Sweller, van Merriënboer & Paas, 1998) this integration process is cognitively demanding and at the expense of mental resources that could otherwise be allocated to the learning process. Sweller et al. call the

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recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Keywords: computational modeling, cognitive modeling, instructional theory, machine learning, learning science, second language learning, mathematics learning, science learning, robust learning, learning theory, knowledge componentsExecutive Summary The volume of research on learning and instruction is enormous. Yet progress in improving educational outcomes has been slow at best. Many learning science results have not been translated into general practice and it appears that most that have been fielded have not yielded significant results in randomized control trials. Addressing the chasm between learning science and educational practice will require massive efforts from many constituencies, but one of these efforts is to develop a theoretical framework that permits a more systematic accumulation of the relevant research base. A key piece in such a theoretical framework is the development of levels of analyses that are fine enough to be supported by cognitive science and cognitive neuroscience, but also at levels appropriate to guide the design of effective educational practices. An ideal scientific solution would be a small set of universal instructional principles that can be applied to produce efficient

. This paper presents a case-based tutor, CECELIA 1.1, that is based on techniques from CELIA, a computer model of case-based apprenticeship learning [Redmond 1992]. The teaching techniques include: interactive, step by step presentation of case solution steps, student predictions of an expert's actions, presentation of the expert's steps, student explanations of the expert's actions, and presentation of the expert's explanation. In addition, CECELIA takes advantage of a technique from VanLehn's [1987] SIERRA -- presenting examples in an order so that solutions only differ by one branch, or disjunct, from previously presented examples. CECELIA relies on its teaching strategy encouraging greater processing of the examples by the student, rather than on embedding great amounts of intelligence in the tutor. CECELIA is implemented using Hypercard on an Apple Macintosh, and has been pilot tested with real students. The tests suggest that the approach can be helpful, but also suggest that el...

Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert-novice differences, and cognitive load. While unguided or minimally-guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half century that consistently indicate that minimally-guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide ‘internal ’ guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described.

Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide “internal ” guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described. Correspondence should be addressed to Paul A. Kirschner, Research Centre